Browse Articles

Sensitivity of leather tanned by ancient techniques to fungal infestation

Modular Synthesis of <i>Neisseria meningitidis</i> Lipooligosaccharide Inner Core Oligosaccharide Library to Identify Broadly Reactive Antigenic Epitopes

ABSTRACT Neisseria meningitidis remains a global health threat and a leading cause of bacterial meningitis, with six serogroups responsible for most epidemic outbreaks. The inner core of its lipooligosaccharide (LOS) is highly conserved across serogroups and represents a promising target for a broad‐spectrum vaccine development. However, its exploration has been limited by the structural complexity of inner core and the lack of homogeneous oligosaccharide library for systematic antigenicity evaluation. Herein, we report the first total synthesis of a highly branched and sterically crowded inner core hexasaccharide via a strategically designed stereoconvergent [(2+3)+1] assembly approach. Central to this achievement is the rational tuning of glycosyl donor–acceptor reactivity, which enables the efficient construction of the sterically congested and challenging α‐Hep‐(1→5)‐Kdo glycosidic linkage. Additionally, a structurally defined 18‐membered library of inner core oligosaccharides was assembled by a modular synthesis approach. Glycan microarray screening with rabbit antisera raised against six epidemic serogroups suggests that three oligosaccharides exhibit broadly cross‐reactive antigenicity, which provides the candidate epitopes for the development of a broad‐spectrum meningococcal vaccine.

Computational design and immunoinformatics validation of a T cell multi-epitope vaccine targeting glioblastoma stem cells

Controlling Near‐Infrared Fluorescence‐to‐Phosphorescence Ratios and Triplet Lifetimes in Rhodium(I) Dimers via Primary and Secondary Coordination Sphere Effects

ABSTRACT Rhodium(I) and other d 8 ‐metals form discrete dimers in which bridging ligands position two square‐planar coordination units in close proximity, enabling metal‐metal interactions. Although the metal‐metal distance is known to modulate absorption and photoluminescence wavelengths, clear synthetic guidelines for controlling intersystem crossing and triplet excited‐state lifetimes remain elusive. We show that homoleptic coordination with four identical bridging di‐isocyanide ligands produces a phosphorescent rhodium(I) dimer emitting in the near‐infrared (NIR)‐II region. In contrast, heteroleptic complexes containing two di‐isocyanide and two di‐phosphine ligands yield rhodium(I) dimers that show mainly NIR‐I fluorescence and NIR‐II phosphorescence. In these heteroleptic systems, the fluorescence‐to‐phosphorescence ratio and triplet lifetime depend on the extent of metal‐metal interactions, which can be tuned by over 0.2 Å through modifications at the di‐isocyanide ligand periphery while preserving the primary coordination sphere. Our results are consistent with a picture in which rigidification of the central bimetallic core arises from changes to both the primary and secondary coordination environments, thereby reducing nonradiative excited‐state relaxation pathways, most notably from the phosphorescent T 1 state. These findings provide guidelines for tuning fluorescence and phosphorescence relevant to imaging and phototherapy, as well as controlling singlet versus triplet photoreactivity in photocatalytic systems for synthetic chemistry and solar energy conversion.

Evaluation of serum progranulin as a biomarker for early detection of neonatal sepsis in a microbiological context

Abstract Background Neonatal sepsis remains a major contributor to neonatal morbidity and mortality worldwide. The nonspecific nature of clinical signs and the limited sensitivity of conventional microbiological methods, such as blood culture, often hinder early diagnosis. This study evaluated the diagnostic performance of serum progranulin (PGRN) in comparison with established biomarkers—procalcitonin (PCT) and C-reactive protein (CRP)—in the early detection of suspected neonatal sepsis before microbiological confirmation, thereby supporting prompt clinical decision-making. Methods: A total of 60 neonates with clinically suspected sepsis and 30 healthy controls were enrolled. Before initiation of antimicrobial therapy, blood samples were collected for microbiological culture, complete blood count, and platelet count. Additional samples were analyzed for CRP, PCT, and PGRN levels using enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy was determined using receiver operating characteristic (ROC) curve analysis. Results: Serum PGRN levels were significantly elevated in the sepsis group compared to controls (77.26 vs. 28.78 ng/mL; Mann–Whitney p  &lt; 0.001). PGRN demonstrated excellent diagnostic accuracy (AUC = 0.986; 95% CI 0.957–1.000; p  &lt; 0.001), outperforming both PCT (AUC = 0.772; 95% CI 0.666–0.877; p  &lt; 0.001) and CRP (AUC = 0.833; 95% CI 0.752–0.915; p  &lt; 0.001). At a cut-off of 35.80 ng/mL, PGRN yielded a sensitivity of 98.31% and a negative predictive value of 96.7%. Combined assessment of PGRN and PCT further enhanced diagnostic sensitivity and specificity. Conclusion: Serum progranulin is a promising biomarker for the early diagnosis of neonatal sepsis and demonstrates superior diagnostic accuracy compared to PCT and CRP. When integrated with conventional microbiological testing and PCT measurement, PGRN can substantially improve early detection rates, enable timely therapeutic intervention, and potentially reduce sepsis-related mortality in neonates.

Switching Between Singlet and Triplet Excitation in Covalent Organic Frameworks for Highly Efficient Photocatalysis

ABSTRACT Singlet (S 1 ) and triplet (T 1 ) excitation serve as the two primary and competing pathways, playing crucial yet entirely distinct roles in the photocatalytic process. Achieving flexible switching between S 1 and T 1 excitation energies has remained a challenge. Herein, three 2D covalent organic frameworks (COFs) with offset stacking angles of 90°, 105°, and 128° were successfully synthesized by integrating folding building blocks within the skeleton. The results show that the strategic offset stacking can harness efficient π–σ attraction, thereby inducing intersystem crossing from S 1 to T 1 state. The face‐to‐face stacked BDT‐HHTP‐COF tends to follow the electron transfer pathway, thereby generating ·O 2 − . In contrast, BDT‐CTC‐COF with the most optimal offset stacking distance produces high concentrations of 1 O 2 , primarily attributing to the energy transfer pathway. Theoretical calculations prove that the BDT‐CTC‐COF can boost Coulomb interaction, trigger intersystem crossing, and accelerate the transfer of the T 1 exciton to the adsorbed O 2 throughout the matrix of the framework. This switch in the mechanistic pathway is critically important, as the highly electrophilic 1 O 2 exhibits superior efficacy in attacking the electron‐rich aromatic ring of toluene, initiating a selective oxidation process that rapidly achieves over 98% degradation and 80% CO 2 mineralization, representing a 1.5‐fold enhancement compared to the electron transfer‐dominated pathway.

Diverse image generation with diffusion models and cross class label learning for polyp classification

Abstract Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing with colorectal cancer (CRC). Colonic polyps, precursors to CRC, can pathologically be classified into two major types: adenomatous (malignant potential) and hyperplastic (benign). Various imaging techniques, such as narrow band imaging (NBI) and white light imaging (WLI), are adopted in capturing polyp-specific features for accurate classification and have different advantages. However, the existing classification techniques mainly rely on a single imaging modality and show limited performance due to data scarcity. Recently, generative artificial intelligence has been gaining prominence in overcoming such issues, especially with various generation-controlling mechanisms using text prompts and images. However, such mechanisms require class labels to make the model respond efficiently to the provided control input. In the colonoscopy domain, such controlling mechanisms are rarely explored; specifically, the text prompt is a completely uninvestigated area. Moreover, the unavailability of expensive class-wise labels for diverse sets of images limits such explorations. This raises the key question of how diverse and clinically meaningful colonoscopy images can be generated in a text-controlled manner from limited annotated data. Therefore, in this work, we develop a novel model, PathoPolyp-Diff , that generates text-controlled synthetic images with diverse characteristics in terms of pathology, imaging modalities, and quality, enabling more effective augmentation of downstream diagnostic models. The proposed model follows a two-stage process: first, the model learns to distinguish polyp from non-polyp characteristics, and then it focuses on pathology-specific features. In the process, we introduce cross-class label learning to make the model learn features from other classes, reducing the burdensome task of data annotation. We validate the effectiveness of text-controlled synthesis and cross-class label learning by performing polyp classification (adenomatous/hyperplastic) with different imaging modalities (NBI/WLI) and text prompts. The experimental results show that incorporating the proposed synthetic images for data augmentation yields an improvement of up to 7.91% in balanced accuracy on a publicly available dataset, highlighting the utility of our approach for enhancing downstream classification performance. Moreover, cross-class label learning achieves a statistically significant improvement of up to 18.33% in balanced accuracy during video-level analysis. The code is available at https://github.com/Vanshali/PathoPolyp-Diff .

EEG channel selection using metaheuristic algorithms in alcoholism detection using optimal wavelet transform

Abstract Alcoholism, habitual and excessive intake of alcoholic beverages, presents a significant disorder that challenges contemporary society; however, alcoholism detection lacks universally acknowledged examinations or protocols. Traditional subjective methods are time-consuming and prone to error. Electroencephalography (EEG) detects alcoholism by analyzing the electrical activity of the brain. An effective EEG channel selection using metaheuristic algorithms (MHAs)-based features are introduced. The EEG signal is decomposed into subbands using a novel optimal wavelet filter bank (OWFB). Each subband is represented using four features: mean, Higuchi’s fractal dimension, log entropy, and Rényi’s entropy. The optimal subband features are investigated using combinations of four MHAs and are six classification models. A publicly available 64-channel EEG dataset of alcoholic and non-alcoholic signals is employed. Among the evaluated combinations, Sparrow search algorithm combined with k-nearest neighbor model (KNN) classifier achieved the highest accuracy of 95.90% and F1-score of 96.80%, closely comparable to using all EEG channels (96.30% accuracy and 96.83% F1-score). Overall, KNN classifier consistently outperformed others, indicating that optimal channel selection can effectively reduce channel redundancy while maintaining high accuracy in alcoholism detection. The optimal channel selection enhances the performance of the ML models and reduces the computational time compared to existing alcoholism detection methods. A Python implementation is available at https://github.com/pramodkachare/EEG_Optimal_Wavelet_MHA .

Investigation on the observational climate variables and the feedback of vegetation dynamics at Ergun City, Inner Mongolia, China

In Situ Construction of Imidazopyridinium Fluorescent Labels for Bioconjugation

ABSTRACT Simple, efficient transformations of fluorogenic nature that proceed under biocompatible conditions without the formation of byproducts are of high interest for in situ labeling and bioconjugation. Following these criteria, we describe in this work the discovery and optimization of imidazopyridinium dyes, obtained through in situ labeling of primary amines with pyridine, quinoline, and isoquinoline aldehydes. The so‐generated dyes are excited with near‐UV to violet light and emit in the orange region of the electromagnetic spectrum with Stokes shifts up to 12170 cm −1 . We employed the reaction to obtain fluorescently labeled amino acids, lipids, and sugars; furthermore, we expanded the scope to proteins and tags for bioimaging. The robustness of the chemistry also allowed us to on‐resin staple peptides, cleanly generating fluorescent, cyclic analogs, which showcase the broad future impact of our transformation.

Catalysis AI Agent Guides Discovering the Universal Design Principle of Cu‐Based Single‐Atom Alloy Catalysts for CO ₂ Electroreduction

ABSTRACT Copper (Cu)‐based single‐atom alloys (SAAs) represent a promising strategy for optimizing the electroreduction of CO 2 (CO 2 R) to multi‐carbon products (C 2+ ). However, the diverse enhancement degrees of C 2+ selectivity brought about by various dopants have not yet been rationalized, which lead to the absence of guidelines for further designing desired Cu‐based SAAs. Herein, guided by the Catalysis AI Agent developed based on large‐scale data + large language model, as well as the Digital Catalysis Platform (the DigCat experimental database), we performed first‐principles calculations to evaluate C 2+ products selectivity trends through identifying the energy barrier of rate‐determining step (RDS) among diverse C‐C coupling pathways. With first‐principles results fed back, Catalysis AI Agent reveals that the element classification in the periodic table of guest metal dopant is essential for establishing robust structure‐selectivity correlations among Cu‐based SAAs. A structural descriptor (φ) is developed and helps to establish a strong correlation among the electronic‐scale structural features, the adsorption strength of C‐C coupling precursors, and the macroscopic C 2+ products selectivity. A universal design principle based on φ for Cu‐based SAAs enables the rapid and qualitative evaluation of C 2+ selectivity, which is fully supported by most of the experimental references and our experimental verification.

Building-level energy prediction and control based on BIM and IIoT technologies

Regioselective Synthesis of Benzylsilanols from Kobayashi‐Type Reagents via C(sp ³ )─H Bond Activating 1,4‐Palladium Migration

ABSTRACT Organosilanes constitute a useful class of compounds in various fields of research in modern organic chemistry. Discovery of unexplored reactivity and utility of organosilanes is therefore highly important to further enhance their values. In this context, a palladium‐catalyzed regioselective synthesis of benzylsilanols from Kobayashi‐type reagents has been developed through a mechanistically distinct approach involving 1,4‐palladium migration followed by intramolecular transmetalation instead of conventional aryne formation. The reaction can be combined with various prior transition‐metal‐catalyzed processes and the resulting benzylsilanols have been utilized as effective benzyl nucleophiles for the Hiyama–Denmark cross‐coupling reactions with aryl halides under simple conditions. The mechanistic experiments have also been carried out, showing that the 1,4‐palladium migration goes through a palladacycle intermediate via turnover‐limiting deprotonative C(sp 3 )─H bond activation.

Demographic and emission drivers of household air pollution in biomass using communities

Abstract This study investigates household air pollution from biomass combustion by integrating a household survey, experimental stove testing, and data-driven analysis. A structured survey of 1200 households captured socio-economic conditions, cooking practices, and health outcomes, showing that 57.4% rely on agriculture and 14.8% report health symptoms linked to traditional stove use, while 33.1% have adopted improved stoves. Experimental testing evaluated stove performance and emissions under real operating conditions. Results indicate that thermal efficiency peaks at 37% during the simmering phase and declines during cold and hot starts. Emissions were highest during cold start, with CO reaching 123 g/MJ and PM 1172 mg/MJ, but decreased by more than 40% during stable operation, particularly when charcoal was used. Temperature was identified as an important environmental factor, reducing CO and PM concentrations while increasing NO 2 formation. Overall, the findings highlight the combined influence of technology performance, user practices, and environmental conditions on household air pollution. The study underscores the importance of promoting cleaner cooking technologies, improving stove operation, and integrating socio-economic considerations into intervention strategies to reduce health risks and support sustainable household energy transitions.

Replacing Metabolically Unstable Amides With Stable α‐Trifluoromethylamines via Decarboxylative Synthesis

ABSTRACT Metabolically unstable amide bonds are a well‐recognized liability in drug candidates. However, a general and practical strategy to rapidly access their stable bioisosteres directly from common synthetic intermediates is lacking. Here, we report a decarboxylative synthesis of α‐trifluoromethylamines from widely available carboxylic acids. This method seamlessly integrates with existing synthetic routes, providing a modular platform for lead optimization. Mechanistically, this transformation is enabled by two key events: a tetramethyl guanidine (TMG)‐promoted decarboxylation via an electron donor–acceptor (EDA) complex, and subsequent single‐electron oxidation that activates the inert α‐C─H bond of trifluoromethyl groups to generate α‐CF 3 /α‐amino carbon radicals. The method exhibits broad functional group tolerance for a diverse range of carboxylic acids, from prevalent bioactive scaffolds such as amino acids, peptides, and uronic acids to complex drug motif itself. This capability enables the direct construction of N‐aryl‐substituted α‐CF 3 amine bioisosteres using the exact carboxylic acid feedstocks from the original active molecules, thereby establishing a robust and practical approach for drug discovery.

Anopheles ecology and serological evidence of malaria exposure in clinically malaria-free areas of Menoreh Hills Central Java, Indonesia

Azithromycin for Preschoolers with Wheezing in the Emergency Department

Non-uniform endogenous regeneration of olfactory sensory neuron axons across the mouse olfactory bulb

Deprotonative C( <i>sp</i> ³ )/C( <i>sp</i> ² )–H (Multi)Silylation of (Hetero)Arenes Mediated by NaTMP

ABSTRACT The importance of organosilicon compounds in synthetic and materials chemistry has prompted the search for efficient and broadly applicable routes to these invaluable scaffolds, which often depend on scarce transition metals. In contrast, main group‐mediated strategies remain underdeveloped, with those reported generally limited to activated substrates and harsh reaction conditions. Here, a new sodium‐mediated protocol for deprotonative C( sp 3 )/C( sp 2 )–H silylation of (hetero)arenes is presented, which relies on the power of the strongly basic sodium amide NaTMP (TMP = 2,2,6,6‐tetramethylpiperidide) in combination with bulky chlorosilanes. This approach provides direct access to a myriad of silylated aromatic products, including toluene derivatives, non‐activated arenes such as benzene and naphthalene, pyridines and electron‐rich heterocycles. Mechanistic investigations, combining the isolation of key organometallic intermediates with theoretical calculations, underline the complementarity of NaTMP and the electrophilic chlorosilane, and reveal the steric and coordination effects that govern sodiation and silylation steps. Most notably, this protocol extends beyond conventional monosilylation, including orthogonal multisilylation of distinct C( sp 3 ) and C( sp 2 )–H bonds, thereby broadening the scope of main group‐mediated arene functionalization.

Wave propagation and thermal behavior in nonlocal thermoelastic porous media under moving heat sources with three-phase-lag and Green–Naghdi models

Abstract This study examines thermoelastic wave propagation in a nonlocal porous thermoelastic half-space subjected to a moving heat source using two advanced theoretical models: the three-phase-lag (3PHL) heat conduction model and the Green–Naghdi type III theory. Analytical solutions for the physical field variables are derived via normal mode analysis. The work highlights the role of nonlocal effects in porous media and presents a comparative assessment of the two thermoelastic frameworks. Numerical results demonstrate that locality reduces displacement and stress amplitudes, while porosity and phase-lag parameters significantly influence temperature and volume fraction fields. Additionally, the moving heat source modifies wave propagation characteristics and enhances the thermal response near the boundary. These results offer valuable insight into thermally induced deformation in porous materials and are relevant to applications such as laser processing, additive manufacturing, and thermal protection systems.